On the Design of Large-Scale UMTS Mobile Networks Using Hybrid Genetic Algorithms
Bibliographic record
Abstract
Third-generation mobile systems provide access to a wide range of services and enable mobile users to communicate, regardless of their geographical location and their roaming characteristics. Due to the growing number of mobile users and global connectivity, one of the most critical issues regarding the design of universal mobile telecommunications service (UMTS) networks pertains to the assignment of Node Bs to radio network controllers (RNCs), which is an NP-hard problem. Hence, for real-sized mobile networks, this problem cannot be practically solved by using exact methods. This paper proposes a hybrid genetic algorithm (HA) with migration to solve the problem of assigning Bs to RNCs as a design step of large-scale UMTS mobile networks. Computational results obtained from extensive tests confirm the effectiveness of the HA to provide superior solutions compared to other heuristic methods that are well documented in the literature. Such an algorithm is particularly suitable to design large-scale cellular mobile networks with Node Bs whose quantity varies between 100 and 500 and whose the number of RNCs ranges between five and ten.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".